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Soft pneumatic actuators/robotics have received significant interest in the medical and health fields, due to their intrinsic elasticity and simple control strategies for enabling desired interactions. However, current soft hand pneumatic exoskeletons often exhibit uniform deformation, mismatch the profile of the interacting objects, and seldom quantify the assistive effects during activities of daily life (ADL), such as extension angle and predicted joint stiffness. The lack of quantification poses challenges to the effective and sustainable advancement of rehabilitation technology. This paper introduces the design, modeling, and testing of pneumatic bioinspired segmented composite proprioceptive bending actuators (SCPBAs) for hand rehabilitation in ADL tasks. Inspired by human finger anatomy, the actuator's soft-joint–rigid-bone segmented structure provides a superior fit compared to continuous structures in traditional fiber-reinforced actuators (FRAs). A quasi-static model is established to predict the bending angles based on geometric parameters. Quantitative evaluations of predicted joint stiffness and extension angle utilizing proprioceptive bending are performed. Additionally, a soft under-actuated hand exoskeleton equipped with SCPBAs demonstrates their potential in ADL rehabilitation scenarios. [ABSTRACT FROM AUTHOR]

Critical heat flux (CHF) is one of the most concerned thermal hydraulic phenomena in reactor safety analysis. It involves complex two-phase flow heat transfer mechanism, and has not been fully understood, so the prediction of critical heat flux mainly depends on CHF correlations obtained under limited experimental conditions. At present, CHF correlations are generally developed with pressure, mass flux and quality as key independent variables. And correspondingly, the test matrix of a CHF test consists of the above parameters. However, it is impossible to perform CHF tests accurately according to the predetermined quality. In CIAE, a CHF experimental research of a 5 χ 5 uniformly heated rod bundle has been carried out. In the experiment, the inlet temperature of the test section was directly taken as a parameter in the test matrix. The CHF data were achieved by stepwise increasing the heating power. The test conditions covered the pressure of 2.8-15.5 MPa, the mass flux of 845-3533 kg/(m2-s), and the inlet temperature of 100°C-300°C. The test data have been analyzed to obtain the thermal-hydraulic parameter influences on CHF by taking the inlet temperature as a variable. The results indicated that, within the test condition range, under the same inlet temperatures, CHF was hardly affected by pressure, and linearly increased with the increasing mass flux. With the increase of inlet temperature, the enhancement of CHF with the increasing mass flux gradually weakens. And CHF was linearly decreased with the increasing inlet temperature under the same mass flux. By contrast, the parameter influences on CHF were more complex by taking the local quality as a variable. According to the research, it can be concluded that, it has an advantage of simplifying the CHF correlation form to take the inlet temperature of the test section as a variable parameter. The research can provide new ideas for CHF experiment, data analysis and correlation development. [ABSTRACT FROM AUTHOR]

The interdependence of various energy forms and flexible cooperative operation between different units in an integrated energy system (IES) are essential for carbon emission reduction. To address the planning problem of an electric–thermal integrated energy system under low-carbon conditions and to fully consider the low carbon and construction sequence of the integrated energy system, a low-carbon-oriented capacity optimization method for the electric–thermal integrated energy system that considers construction time sequence (CTS) and uncertainty is proposed. A calculation model for the carbon transaction cost under the ladder carbon trading mechanism was constructed, and a multi-stage planning model of the integrated energy system was established with the minimum life cycle cost, considering carbon transaction cost as the objective function, to make the optimal decision on equipment configuration in each planning stage. Finally, a case study was considered to verify the advantages of the proposed capacity optimization method in terms of economy and environmental friendliness through a comparative analysis of different planning cases. Simulation results show that, compared with the scenario of completing planning at the beginning of the life cycle at one time, the proposed low-carbon-oriented capacity optimization method that considers construction time sequence and uncertainty can not only reduce the cost of the integrated energy system, but also help to enhance renewable energy utilization and reduce the system's carbon emissions; the total cost of phased planning is reduced by 11.91% compared to the total cost of one-time planning at the beginning of the year. [ABSTRACT FROM AUTHOR]

In recent years, 3D object detection based on LiDAR point clouds is a key component of autonomous driving. In pursuit of enhancing the accuracy of 3D point cloud feature extraction and point cloud detection, this paper introduces a novel 3D object detection model, termed as Graph Self-Attention-RCNN (GA-RCNN). This model is designed to integrate voxel information and point location information, enhancing the quality of 3D object proposals while maintaining contextual accuracy. The first stage rectifies the previous approach that relied on local features for preselected boxes, overlooking crucial global contextual information. An improved method is suggested in this work, utilizing BEV to capture long-range dependencies via a cross-attention mechanism. The second stage addresses the overreliance on local neighborhood point feature extraction. The Graph Self-Attention Pooling method is proposed, characterized by its dynamic computation of contribution weights for inputs. This enhances the model's flexibility and generalization performance. Extensive evaluations on KITTI and Waymo datasets demonstrate GA-RCNN's superior accuracy compared to other methods, affirming its efficacy in 3D object detection. [ABSTRACT FROM AUTHOR]

As a friendly tool, the soft gripper can be used directly for grasping vulnerable objects in underwater environments. These tasks are generally performed by teleoperation based on the visual feedback, rather than the soft actuators' sensing information. However, vision sensors' function may be restricted in some complex underwater environments with poor visibility and narrow spaces. This will greatly reduce the efficiency of the underwater operations. Therefore, soft actuators strongly require an organism‐like perception system to sense environmental stimuli and can be applied to complex underwater environments. To address the problem, a multidirectional external perception soft actuator based on two flexible optical waveguide sensors is developed and machine learning methods are utilized to build its perceptual model herein. The experimental results indicate that the soft actuator can recognize 12 contact positions based on the sensing model, and the identifying accuracy is up to 99.82%. Additionally, according to the contact location feedback, teleoperation can be more efficiently completed in unknown underwater environments. [ABSTRACT FROM AUTHOR]

We have studied two regioisomeric terrylenediimide (TDI) dimers in which the 1-positions of two TDIs are linked via 1,3- or 1,4-phenylene spacers, mTDI2 and pTDI, respectively. The nature and the dynamics of the multiexciton state are tuned by altering the through-bond electronic couplings in the ground and excited states and by changing the solvent environment. Our results show that controlling the electronic coupling between the two chromophores by an appropriate choice of linker can result in independent triplet state formation, even though the initial correlated triplet pair state is confined to a dimer. Moreover, even in polar solvents, if the electronic coupling is strong, the correlated triplet pair state is observed prior to symmetry-breaking charge separation. These results point out the close relationship between the singlet, correlated triplet pair, and charge transfer states in molecular dimers. [ABSTRACT FROM AUTHOR]